Duisburg was one of the counties with the lowest participation (54 %). Only Gelsenkirchen was worse (52 %). Participation was higher in Cologne, with 66 %.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 52.29 60.68 63.36 62.86 65.37 74.34
The whole Ruhr area was the “heart chamber” of SPD support in NRW. With 22 %, support was among the highest in Duisburg.
In Cologne, only 15 % voted SPD.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 11.37 15.07 16.66 17.44 20.66 23.92
The vote share for the AfD was 17 % in Duisburg, higher than in most other counties and the state average.
In Cologne, only 7 % voted AfD.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.798 11.450 13.342 13.326 15.576 21.663
After the European Election 2019, the EPP, the S&D and the Renew faction supported von der Leyen as Head of the EU Commission.
Vote shares for parties belonging to factions that supported vdL were lower in larger cities, mainly driven by the weakness of the CDU in cities.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 43.55 53.60 57.23 56.88 59.51 69.55
stargazer(afdmr1, afdmr2, afdmr3, afdmr4, afdmr5, afdmr6, type = "html", title = "Regression Results")
| Dependent variable: | ||||||
| prozent_AfD | ||||||
| (1) | (2) | (3) | (4) | (5) | (6) | |
| ruhrgebiet | 3.109*** | 3.049*** | 3.798*** | 1.283 | 1.315 | 1.183 |
| (0.853) | (0.883) | (0.940) | (0.894) | (0.887) | (1.006) | |
| grenzgebiet | -0.379 | -0.856 | -0.557 | -0.835 | -0.758 | |
| (1.256) | (1.245) | (1.003) | (1.016) | (1.060) | ||
| bvdichte | -0.001* | -0.003*** | -0.003*** | -0.003*** | ||
| (0.0005) | (0.001) | (0.001) | (0.001) | |||
| sgbq | 0.090*** | 0.074*** | 0.079*** | |||
| (0.017) | (0.021) | (0.028) | ||||
| ohnehauptq | 0.325 | 0.307 | ||||
| (0.242) | (0.252) | |||||
| auslanderq | -0.047 | |||||
| (0.164) | ||||||
| Constant | 12.446*** | 12.506*** | 13.363*** | 8.509*** | 7.637*** | 7.965*** |
| (0.454) | (0.499) | (0.652) | (1.060) | (1.236) | (1.690) | |
| Observations | 53 | 53 | 53 | 53 | 53 | 53 |
| R2 | 0.207 | 0.208 | 0.266 | 0.535 | 0.552 | 0.553 |
| Adjusted R2 | 0.191 | 0.176 | 0.221 | 0.496 | 0.505 | 0.495 |
| Residual Std. Error | 2.797 (df = 51) | 2.823 (df = 50) | 2.745 (df = 49) | 2.207 (df = 48) | 2.189 (df = 47) | 2.211 (df = 46) |
| F Statistic | 13.283*** (df = 1; 51) | 6.569*** (df = 2; 50) | 5.918*** (df = 3; 49) | 13.811*** (df = 4; 48) | 11.591*** (df = 5; 47) | 9.485*** (df = 6; 46) |
| Note: | p<0.1; p<0.05; p<0.01 | |||||
In Oberhausen, the residual is largest, indicating more AfD support than expected by the structural data.
In Münster, the residual is smalles, indicating less AfD support than expected by the structural data.
max(resid(afdmr6))
## [1] 3.91558
outlier <- dat2[which.max(resid(afdmr6)), ]
outlier$name
## [1] "Oberhausen, Stadt"
min(resid(afdmr6))
## [1] -6.392713
outlier <- dat2[which.min(resid(afdmr6)), ]
outlier$name
## [1] "Münster, Stadt"
plot(afdmr6)